{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W80uU6hA8j6i40reOHAGigL/lwBuBU8C98bhfEAVd3gH8GHiIqK4C8fnfpLyY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\n+rpHm84cFk8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234c56b9e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 随机生成1000个点，围绕在y=0.1x+0.3的直线周围\n",
    "num_points = 1000\n",
    "vectors_set = []\n",
    "for i in range(num_points):\n",
    "    x1 = np.random.normal(0.0, 0.55)\n",
    "    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)\n",
    "    vectors_set.append([x1, y1])\n",
    "\n",
    "# 生成一些样本\n",
    "x_data = [v[0] for v in vectors_set]\n",
    "y_data = [v[1] for v in vectors_set]\n",
    "\n",
    "plt.scatter(x_data,y_data,c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W = [ 0.33527207] b = [ 0.] loss = 0.104051\n",
      "W = [ 0.27314326] b = [ 0.29449064] loss = 0.00983059\n",
      "W = [ 0.22196499] b = [ 0.29603973] loss = 0.00535701\n",
      "W = [ 0.18581694] b = [ 0.29731578] loss = 0.00312445\n",
      "W = [ 0.16028048] b = [ 0.29821706] loss = 0.00201028\n",
      "W = [ 0.14224048] b = [ 0.29885375] loss = 0.00145424\n",
      "W = [ 0.12949628] b = [ 0.29930356] loss = 0.00117674\n",
      "W = [ 0.12049324] b = [ 0.29962131] loss = 0.00103825\n",
      "W = [ 0.11413313] b = [ 0.29984578] loss = 0.000969141\n",
      "W = [ 0.10964008] b = [ 0.30000436] loss = 0.000934649\n",
      "W = [ 0.106466] b = [ 0.30011639] loss = 0.000917436\n",
      "W = [ 0.10422371] b = [ 0.30019554] loss = 0.000908845\n",
      "W = [ 0.10263965] b = [ 0.30025145] loss = 0.000904558\n",
      "W = [ 0.10152061] b = [ 0.30029094] loss = 0.000902419\n",
      "W = [ 0.10073008] b = [ 0.30031884] loss = 0.000901351\n",
      "W = [ 0.10017161] b = [ 0.30033854] loss = 0.000900818\n",
      "W = [ 0.09977709] b = [ 0.30035248] loss = 0.000900552\n",
      "W = [ 0.09949838] b = [ 0.30036232] loss = 0.000900419\n",
      "W = [ 0.09930149] b = [ 0.30036926] loss = 0.000900353\n",
      "W = [ 0.0991624] b = [ 0.30037418] loss = 0.00090032\n",
      "W = [ 0.09906414] b = [ 0.30037764] loss = 0.000900303\n"
     ]
    }
   ],
   "source": [
    "# 生成1维的W矩阵，取值是[-1,1]之间的随机数\n",
    "with tf.name_scope('weight'):\n",
    "    W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')\n",
    "# 生成1维的b矩阵，初始值是0\n",
    "with tf.name_scope('bias'):\n",
    "    b = tf.Variable(tf.zeros([1]), name='b')\n",
    "# 经过计算得出预估值y\n",
    "with tf.name_scope('y'):\n",
    "    y = W * x_data + b\n",
    "\n",
    "# 以预估值y和实际值y_data之间的均方误差作为损失\n",
    "with tf.name_scope('loss'):\n",
    "    loss = tf.reduce_mean(tf.square(y - y_data), name='loss')\n",
    "# 采用梯度下降法来优化参数\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.5)\n",
    "# 训练的过程就是最小化这个误差值\n",
    "train = optimizer.minimize(loss, name='train')\n",
    "\n",
    "sess = tf.Session()\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "# 初始化的W和b是多少\n",
    "print (\"W =\", sess.run(W), \"b =\", sess.run(b), \"loss =\", sess.run(loss))\n",
    "# 执行20次训练\n",
    "for step in range(20):\n",
    "    sess.run(train)\n",
    "    # 输出训练好的W和b\n",
    "    print (\"W =\", sess.run(W), \"b =\", sess.run(b), \"loss =\", sess.run(loss))\n",
    "writer = tf.train.SummaryWriter(\"logs/\", sess.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w9Qt/Fb5fQWlIBqC1tZUHt28nn8/TOmMG38eGWbaG709gKzk+MzJC/8BAoeok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\n3CyYmZlZ0v8AuUjJ8/FzAf8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234c96adb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_data,y_data,c='r')\n",
    "plt.plot(x_data,sess.run(W)*x_data+sess.run(b))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
